{"id":"W2272231993","doi":"10.1063/1.4941709","title":"An automated microfluidic system for screening <i>Caenorhabditis elegans</i> behaviors using electrotaxis","year":2016,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Caenorhabditis elegans; Microfluidics; Computer science; Biology; High-throughput screening; Genetic screen; Optogenetics; Chemical genetics; Computational biology; Neuroscience; Phenotype; Nanotechnology; Bioinformatics; Genetics; Gene; Materials science; Small molecule","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003625842,0.0005284197,0.0005225654,0.0006032913,0.0003117447,0.0003331247,0.0008836814,0.0004129404,0.001026221],"category_scores_gemma":[0.000292289,0.0003409141,0.0003614222,0.0002557169,0.0002142382,0.0003115346,0.0004606823,0.0003104696,0.0004250886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003430504,"about_ca_system_score_gemma":0.0006031515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195676,"about_ca_topic_score_gemma":0.00158157,"domain_scores_codex":[0.9996037,0.00002493914,0.00003811849,0.0001160394,0.0001773113,0.00003985153],"domain_scores_gemma":[0.9997734,0.00004884355,0.00005842235,0.00003404451,0.00005743361,0.00002779372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008969885,0.0000777686,0.001026549,0.0001554508,0.00002352467,0.00006339532,0.00002428619,0.0006419102,0.9704525,0.0003319893,0.001484817,0.02562826],"study_design_scores_gemma":[0.00008771621,0.0008110849,0.01087659,0.00003187866,0.00009072971,0.0005667805,0.00001667121,0.02761256,0.9367651,0.0001484886,0.0228574,0.0001350347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.441987,0.00262546,0.5147475,0.0005885525,0.0006073235,0.001768799,0.006775306,0.02525767,0.005642404],"genre_scores_gemma":[0.507759,0.001331814,0.4791965,0.0005600037,0.0001294731,0.001884807,0.003116361,0.0001694587,0.005852491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001195676,"threshold_uncertainty_score":0.003433049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383632528984908,"score_gpt":0.2716318545115843,"score_spread":0.2577955292217353,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}